Culturally-tailored text-messaging intervention for smoking cessation in rural American Indian communities: Rationale, design, and methods

Abstract

Background American Indian (AI) and Alaska Native (AN) communities experience disproportionately high rates of tobacco use when compared to the overall U.S. population, especially among rural populations. Methods We implemented a single-blind, randomized clinical trial of a text messaging-based smoking cessation intervention through the tobacco quitlines of five states (Alaska, Minnesota, New Mexico, Oklahoma, and Wisconsin) with high percentages of AI residents. We partnered with state quitlines and Optum, a multi-state entity that manages quitlines. Participants who called the quitlines and identified as AI/AN were given the option to enroll in this trial. Upon consent, they were randomly assigned to either the standard quitline program (control) or a program culturally tailored for AI/ANs (intervention), which used a text messaging intervention to encourage smoking cessation. We adapted the text messages based on key informant and focus group input. Baseline data was analyzed for differences across age, sex, and the Fagerström Test for Nicotine Dependence. Results We recruited n = 487 AIs into the trial. Participants had an average age of 41.9 years (SD = 11.7) and 66% were female. The average Fagerström Test for Nicotine Dependence score was 5.38 (SD = 2.37). The intervention and control arms did not significantly differ across any of the baseline characteristics. Conclusion Implementation of this trial illustrated important lessons in adapting, implementing, and evaluating trials in collaboration with AI communities and local and national organizations. This work will inform future efforts to implement culturally-tailored interventions with AI/ANs and advance our knowledge about adapting and implementing smoking cessation interventions.

Coming together for something good: recommendations from a scoping review for dissemination and implementation science to improve indigenous substance use disorder treatment

“…The need for flexibility in participant engagement strategies, and the ability to pivot based on community-generated solutions for health equity in substance use accessibility and capacity was, in fact, described across studies. For example, Orr and colleagues ( 47 ) initially intended to recruit AI/AN college students for their tobacco cessation program. However, recruitment efforts led to only 9 college students enrolling.…”

“…Out of the 20 studies reviewed, ten (50%) of them reported outcomes related to intervention effectiveness ( 1 , 34 , 36 , 38 , 40 , 41 , 45 , 48–50 ). There were several studies where the primary outcome was tobacco cessation [ n = 7; ( 32 , 36 , 38 , 45 , 47–49 )]. In a tobacco cessation program for pregnant Aboriginal women, self-reported 12-week 7-day point-prevalence abstinence was 13.6% ( 38 ).…”

“…Interventions addressing alcohol and other substance use also resulted in favorable outcomes. In a study assessing the effectiveness of Motivational Interviewing and Community Reinforcement Approach among a Tribal community in the Southwest United States, at 8 months, percent days abstinent had increased for both alcohol and cannabis, as well as other substances [excluding tobacco; ( 47 )]. In the primary outcome clinical trial of Motivational Interviewing and Community Reinforcement Approach, participants in both the treatment, and treatment as usual (TAU), improved in percent days abstinent and substance use severity [MICRA PDA =72.63%, TAU = 73.62%; ( 1 )].…”

“…Sixty percent of the studies described processes and outcomes related to Adoption [ n = 12; ( 1 , 32 , 36–38 , 40–42 , 44 , 47–49 )]. At the staff level, authors highlighted strategies, for and barriers to, Adoption.…”

“…Facilitators of Implementation were described at both the internal (i.e., organizational) and external levels, with 60% of studies touching upon this [ n = 12; ( 1 , 33–35 , 37 , 38 , 40–43 , 46 , 47 , 49 )]. Organizations face an array of internal challenges (e.g., shifting priorities and reduced funding) along with external factors that impact what types of interventions may be supported or can be reimbursed.…”